Oxford Bulletin of Economics and Statistics

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  • Biases and Strategic Behaviour in Performance Evaluation: The Case of the FIFA's best soccer player award

    In this paper, we study biases in performance evaluation by analysing votes for the FIFA Ballon d'Or award for best soccer player, the most prestigious award in the sport. Our findings suggest that ‘similarity’ biases are substantial, with jury members disproportionately voting for candidates from their own country, own national team, own continent and own league team. Further, we show that the impact of such biases on the total number of votes a candidate receives is fairly limited and hence is likely to affect the outcome of this competition only on rare occasions where the difference in quality between the leading candidates is small. Finally, analysing the incidence of ‘strategic voting’, we find jury members who vote for one leading candidate are more, rather than less, likely to also give points to his main competitor, as compared with neutral jury members. We discuss the implications of our findings for the design of awards, elections and performance evaluation systems in general and for the FIFA Ballon d'Or award in particular.

  • Mergers Along the Global Supply Chain: Information Technologies and Routine Tasks

    This paper empirically analyses how the adoption of Information Technologies (IT) has changed the organization of global supply chains. We focus on international mergers, which are a growing and important component of foreign direct investment. We use data on North–South mergers and acquisitions (M&As). We show that the effect of IT adoption on the number of vertical M&As is decreasing with the routine intensity of the industry. Our interpretation is that the IT revolution enabled new monitoring mechanisms. This allowed Northern headquarters to better monitor suppliers, especially those in less routine‐intensive industries –which were harder to monitor before.

  • Solving Models with Jump Discontinuities in Policy Functions

    We compare global methods for solving models with jump discontinuities in the policy function. We find that differences between value function iteration (VFI) and other methods are economically significant and Euler equation errors fail to be a sufficient measure of accuracy in such models. VFI fails to accurately identify both the location and size of jump discontinuities, while the endogenous grid method (EGM) and the finite element method (FEM) are much better at approximating this class of models. We further show that combining VFI with a local interpolation step (VFI‐INT) is sufficient to obtain accurate approximations. The combination of computational speed, relatively easy implementation and adaptability make VFI‐INT especially suitable for approximating models with jump discontinuities in policy functions: while EGM is the fastest method, it is relatively complex to implement; implementation of VFI‐INT is relatively straightforward and it is much faster than FEM.

  • Issue Information
  • Low Paid Employment in Britain: Estimating State‐Dependence and Stepping Stone Effects

    Using 18 waves of the British Household Panel Study, this paper examines state‐dependence and stepping stone effects of low pay. The results show that both state‐dependence and stepping stone effects of low pay are present. However, there is no evidence to support a low‐pay no‐pay cycle. The introduction of the national minimum wage does not appear to have affected state‐dependence and stepping stone effects of low pay.

  • TIPS and the VIX: Spillovers from Financial Panic to Breakeven Inflation in an Automated, Nonlinear Modeling Framework

    This paper examines the determinants of the breakeven inflation rate (BEI) on U.S. Treasury Inflation Protected Securities. After controlling for several measures of liquidity, inflation expectations and inflation uncertainty; financial fear itself (proxied with the Volatility Index or VIX) remains a primary influence on BEI. To delve into the mechanism underlying this association, the VIX is decomposed, using intraday data, into conditional variance and the variance premium capturing risk aversion. Aside from the 2008 crisis, most of the effect emanated from the variance premium. Following the crisis, indicators of bank insolvency risk gain prominence as well. Lastly, an automated nonlinear model finds convex effects of variance, and diminishing returns to insolvency risk and liquidity.

  • To Pool or Not to Pool: Revisited

    This paper provides a new comparative analysis of pooled least squares and fixed effects (FE) estimators of the slope coefficients in the case of panel data models when the time dimension (T) is fixed while the cross section dimension (N) is allowed to increase without bounds. The individual effects are allowed to be correlated with the regressors, and the comparison is carried out in terms of an exponent coefficient, δ, which measures the degree of pervasiveness of the FE in the panel. The use of δ allows us to distinguish between poolability of small N dimensional panels with large T from large N dimensional panels with small T. It is shown that the pooled estimator remains consistent so long as δ<1/2.

  • Data‐Driven Identification Constraints for DSGE Models

    We propose imposing data‐driven identification constraints to alleviate the multimodality problem arising in the estimation of poorly identified dynamic stochastic general equilibrium models under non‐informative prior distributions. We also devise an iterative procedure based on the posterior density of the parameters for finding these constraints. An empirical application to the Smets and Wouters () model demonstrates the properties of the estimation method, and shows how the problem of multimodal posterior distributions caused by parameter redundancy is eliminated by identification constraints. Out‐of‐sample forecast comparisons as well as Bayes factors lend support to the constrained model.

  • Nowcasting Indian GDP

    Nowcasting has become a useful tool for making timely predictions of gross domestic product (GDP) in a data‐rich environment. However, in developing economies this is more challenging due to substantial revisions in GDP data and the limited availability of predictor variables. Taking India as a leading case, we use a dynamic factor model nowcasting method to analyse these two issues. Firstly, we propose to compare nowcasts of the first release of GDP to those of the final release to assess differences in their predictability. Secondly, we expand a standard set of predictors typically used for nowcasting GDP with nominal and international series, in order to proxy the variation in missing employment and service sector variables in India. We find that the factor model improves over several benchmarks, including bridge equations, but only for the final GDP release and not for the first release. Also, the nominal and international series improve predictions over and above real series. This suggests that future studies of nowcasting in developing economies which have similar issues of data revisions and availability as India should be careful in analysing first‐ vs. final‐release GDP data, and may find that predictions are improved when additional variables from more timely international data sources are included.

  • The Long‐Term Effects of Legalizing Divorce on Children

    We estimate the effect of divorce legalization on the long‐term well‐being of children, by exploiting the different timing of divorce legalization across Europe. We compare the adult outcomes of cohorts raised when divorce was banned with those of cohorts raised after divorce was legalized in the same country. We also have ‘control’ countries where all cohorts were exposed (or not exposed) to legal divorce as children. We find that women who grew up under legal divorce have lower earnings and income and worse health as adults compared with women who grew up under illegal divorce. These negative effects are not found for men.

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